Improved PCA model for multiple fault detection, isolation and reconstruction of sensors in nuclear power plant

Yu, Yue; Peng, Minjun; Wang, Hang; Ma, Zhihui; Li, Wei

doi:10.1016/j.anucene.2020.107662

Cited by 52 publications

(28 citation statements)

References 16 publications

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“…Then, the DDML methods of the unsupervised learning type for the FDD in the NPP include the clustering (Baraldi et al, 2013;Wang et al, 2021) and PCA (Ayodeji et al, 2018;Ling et al, 2020;Yu et al, 2020;Wang et al, 2021) techniques as shown in Table 2.…”

Section: Unsupervised and Reinforcement Learning Methodsmentioning

confidence: 99%

“…In 2018, Ayodeji et al (2018) operated the PCA with the radial basis function (RBF) for the transient scenarios in the NPP. Then, Yu et al (2020) detected the sensor faults with the PCA approach. Afterward, Ling et al (2020) presented the FDD of the reactor coolant system in the NPP.…”

Section: Principal Component Analysis Approachmentioning

confidence: 99%

“…5 | Latest applications of the data-driven machine learning (DDML) for the fault diagnosis and detection (FDD) of the nuclear power plant (NPP) component. (Baraldi et al, 2013;Zhang et al, 2020), reactor coolant pump (Di et al, 2013;Liu and Zio, 2017), steam generator (Lu and Upadhyaya, 2005;Zhao and Upadhyaya, 2005;Razavi-Far et al, 2009;Li et al, 2012;Ayodeji and Liu, 2018b), control rod (Moshkbar-Bakhshayesh, 2020Oluwasegun and Jung, 2020), turbine generator (Biet, 2012;Zhang et al, 2013), bearing (Ren et al, 2016;Zhao and Wang, 2018;Miki and Demachi, 2020), and sensors (Upadhyaya et al, 2003;Mandal et al, 2017a,b;Choi and Lee, 2020;Nguyen et al, 2020;Yu et al, 2020;Wang et al, 2021) are captured by different modeling techniques. Initially, Baraldi et al (2013) tested the clustering for the FDD in the pressurizer in the NPP.…”

Section: Latest Applications Of Ddml For Fdd In the Npp Systemmentioning

confidence: 99%

“…Furthermore, the SVM plus sparse, DNN, and LSTM approaches for the FDD of the roller bearing were also proposed, respectively (Ren et al, 2016;Zhao and Wang, 2018;Miki and Demachi, 2020). Lastly, various techniques, including the PCA, GMDH, SVD, DBN, RNN, and clustering, are carried out for the sensor faults correspondingly in the NPP as shown in Table 4 (Upadhyaya et al, 2003;Mandal et al, 2017a,b;Choi and Lee, 2020;Nguyen et al, 2020;Yu et al, 2020;Wang et al, 2021).…”

Section: Referencesmentioning

confidence: 99%

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Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Zhou

Liu

2021

Front. Energy Res.

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Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded.

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Section: Unsupervised and Reinforcement Learning Methodsmentioning

confidence: 99%

Section: Principal Component Analysis Approachmentioning

confidence: 99%

Section: Latest Applications Of Ddml For Fdd In the Npp Systemmentioning

confidence: 99%

Section: Referencesmentioning

confidence: 99%

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Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Zhou

Liu

2021

Front. Energy Res.

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“…The use of statistical approaches for the construction of a soft sensor is widely studied in the literature; an example of this is the research developed in [ 11 ] in which the authors developed a methodology for the construction of a soft-sensor based on principal component analysis (PCA) for the detection of sensor failures. The soft-sensor model was built based on historical data taken from an actual nuclear power plant.…”

Section: Introductionmentioning

confidence: 99%

Using Soft Sensors as a Basis of an Innovative Architecture for Operation Planning and Quality Evaluation in Agricultural Sprayers

Penaloza

Oliveira

Cruvinel

2021

Sensors

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One of the major problems facing humanity in the coming decades is the production of food on a large scale. The production of large quantities of food must be conducted in a sustainable and responsible manner for nature and humans. In this sense, the appropriate application of agricultural pesticides plays a fundamental role since pesticide application in a qualified manner reduces human and environmental risks as well as the costs of food production. Evaluation of the quality of application using sprayers is an important issue, and several quality descriptors related to the average diameter and distribution of droplets are used. This paper describes the construction of a data-driven soft sensor using the parametric principal component regression (PCR) method based on principal component analysis (PCA), which works in two configurations: with the input being the operating conditions of the agricultural boom sprayers and its outputs being the prediction of the quality descriptors of spraying, and vice versa. The soft sensor provides, in one configuration, estimates of the quality of pesticide application at a certain time and, in the other, estimates of the appropriate sprayer-operating conditions, which can be used for control and optimization of the processes in pesticide application. Full cone nozzles are used to illustrate a practical application as well as to validate the usefulness of the soft sensor designed with the PCR method. The selection of historical data, exploration, and filtering of data, and the structure and validation of the soft sensor are presented. For comparison purposes, the results with the well-known nonparametric k-Nearest Neighbor (k−NN) regression method are presented. The results of this research reveal the usefulness of soft sensors in the application of agricultural pesticides and as a knowledge base to assist in agricultural decision-making.

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Research on Electricity Sales Forecast Model Based on Big Data

Jian

Chen

et al. 2021

Cyberspace Safety and Security

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Improved PCA model for multiple fault detection, isolation and reconstruction of sensors in nuclear power plant

Cited by 52 publications

References 16 publications

Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Data-Driven Machine Learning for Fault Detection and Diagnosis in Nuclear Power Plants: A Review

Using Soft Sensors as a Basis of an Innovative Architecture for Operation Planning and Quality Evaluation in Agricultural Sprayers

Research on Electricity Sales Forecast Model Based on Big Data

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